1. Field of the Invention
This invention relates generally to data mining and, more particularly, to methods and computer program products for excluding variations attributable to equipment from split analysis procedures.
2. Description of Background
Existing data mining systems and methods such as decision trees, multidimensional data sources, and data mining work flows have several limitations. In particular, a decision tree may be employed to analyze data structures for the purpose of revealing relationships and patterns. One or more dependent or target variables are identified which define the shape of the tree. Analytical techniques and statistical methods are applied to the tree in order to express revealed relationships and patterns in the form of models or scores. Analytical techniques and statistical methods include, for example, segmentation, split analysis, classification, and estimation.
Data mining may be employed in the context of manufacturing processes such as semiconductor wafer fabrication. For example, wafers in a 300 mm semiconductor fabrication facility will undergo a long sequence of process steps. In order to optimize device performance and improve yield, each individual step has to meet a certain set of target values. Analytical techniques such as split analysis or design of experiments are often used to improve individual processes. Conclusions derived from split analysis need to be as reliable as practicable because these conclusions may impact business decisions.
The results of split analysis are often influenced or confounded by equipment variations such as wafer order effect, variations among a set of tools or chambers, or other types of equipment variations. These equipment variations often create misleading analysis results. For example, one process split results in good wafers, but all wafers in this split are then processed by a defective chamber. Analysis conclusions derived from these wafers will be misleading unless equipment variations are excluded.
One possible solution for improving the reliability of conclusions derived from split analysis is to implement wafer order randomization prior to the experiment. However, this technique is limited by sample size, is complex and difficult to implement on every lot in the development stage, and is not capable of excluding equipment variations. Wafer experiments are expensive to carry out, especially if these experiments need to be performed across a multiplicity of different lots.
A need therefore exists for improved split analysis procedures. A solution that addresses, at least in part, the above and other shortcomings is desired.